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  <div class="section" id="clipped-proximal-policy-optimization">
<h1>Clipped Proximal Policy Optimization<a class="headerlink" href="#clipped-proximal-policy-optimization" title="Permalink to this headline">¶</a></h1>
<p><strong>Actions space:</strong> Discrete | Continuous</p>
<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/pdf/1707.06347.pdf">Proximal Policy Optimization Algorithms</a></p>
<div class="section" id="network-structure">
<h2>Network Structure<a class="headerlink" href="#network-structure" title="Permalink to this headline">¶</a></h2>
<img alt="../../../_images/ppo.png" class="align-center" src="../../../_images/ppo.png" />
</div>
<div class="section" id="algorithm-description">
<h2>Algorithm Description<a class="headerlink" href="#algorithm-description" title="Permalink to this headline">¶</a></h2>
<div class="section" id="choosing-an-action-continuous-action">
<h3>Choosing an action - Continuous action<a class="headerlink" href="#choosing-an-action-continuous-action" title="Permalink to this headline">¶</a></h3>
<p>Same as in PPO.</p>
</div>
<div class="section" id="training-the-network">
<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
<p>Very similar to PPO, with several small (but very simplifying) changes:</p>
<ol class="arabic">
<li><p>Train both the value and policy networks, simultaneously, by defining a single loss function,
which is the sum of each of the networks loss functions. Then, back propagate gradients only once from this unified loss function.</p></li>
<li><p>The unified network’s optimizer is set to Adam (instead of L-BFGS for the value network as in PPO).</p></li>
<li><p>Value targets are now also calculated based on the GAE advantages.
In this method, the <span class="math notranslate nohighlight">\(V\)</span> values are predicted from the critic network, and then added to the GAE based advantages,
in order to get a <span class="math notranslate nohighlight">\(Q\)</span> value for each action. Now, since our critic network is predicting a <span class="math notranslate nohighlight">\(V\)</span> value for
each state, setting the <span class="math notranslate nohighlight">\(Q\)</span> calculated action-values as a target, will on average serve as a <span class="math notranslate nohighlight">\(V\)</span> state-value target.</p></li>
<li><p>Instead of adapting the penalizing KL divergence coefficient used in PPO, the likelihood ratio
<span class="math notranslate nohighlight">\(r_t(\theta) =\frac{\pi_{\theta}(a|s)}{\pi_{\theta_{old}}(a|s)}\)</span> is clipped, to achieve a similar effect.
This is done by defining the policy’s loss function to be the minimum between the standard surrogate loss and an epsilon
clipped surrogate loss:</p>
<p><span class="math notranslate nohighlight">\(L^{CLIP}(\theta)=E_{t}[min(r_t(\theta)\cdot \hat{A}_t, clip(r_t(\theta), 1-\epsilon, 1+\epsilon) \cdot \hat{A}_t)]\)</span></p>
</li>
</ol>
<dl class="class">
<dt id="rl_coach.agents.clipped_ppo_agent.ClippedPPOAlgorithmParameters">
<em class="property">class </em><code class="sig-prename descclassname">rl_coach.agents.clipped_ppo_agent.</code><code class="sig-name descname">ClippedPPOAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/clipped_ppo_agent.html#ClippedPPOAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.clipped_ppo_agent.ClippedPPOAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>policy_gradient_rescaler</strong> – (PolicyGradientRescaler)
This represents how the critic will be used to update the actor. The critic value function is typically used
to rescale the gradients calculated by the actor. There are several ways for doing this, such as using the
advantage of the action, or the generalized advantage estimation (GAE) value.</p></li>
<li><p><strong>gae_lambda</strong> – (float)
The <span class="math notranslate nohighlight">\(\lambda\)</span> value is used within the GAE function in order to weight different bootstrap length
estimations. Typical values are in the range 0.9-1, and define an exponential decay over the different
n-step estimations.</p></li>
<li><p><strong>clip_likelihood_ratio_using_epsilon</strong> – (float)
If not None, the likelihood ratio between the current and new policy in the PPO loss function will be
clipped to the range [1-clip_likelihood_ratio_using_epsilon, 1+clip_likelihood_ratio_using_epsilon].
This is typically used in the Clipped PPO version of PPO, and should be set to None in regular PPO
implementations.</p></li>
<li><p><strong>value_targets_mix_fraction</strong> – (float)
The targets for the value network are an exponential weighted moving average which uses this mix fraction to
define how much of the new targets will be taken into account when calculating the loss.
This value should be set to the range (0,1], where 1 means that only the new targets will be taken into account.</p></li>
<li><p><strong>estimate_state_value_using_gae</strong> – (bool)
If set to True, the state value will be estimated using the GAE technique.</p></li>
<li><p><strong>use_kl_regularization</strong> – (bool)
If set to True, the loss function will be regularized using the KL diveregence between the current and new
policy, to bound the change of the policy during the network update.</p></li>
<li><p><strong>beta_entropy</strong> – (float)
An entropy regulaization term can be added to the loss function in order to control exploration. This term
is weighted using the <span class="math notranslate nohighlight">\(eta\)</span> value defined by beta_entropy.</p></li>
<li><p><strong>optimization_epochs</strong> – (int)
For each training phase, the collected dataset will be used for multiple epochs, which are defined by the
optimization_epochs value.</p></li>
<li><p><strong>optimization_epochs</strong> – (Schedule)
Can be used to define a schedule over the clipping of the likelihood ratio.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

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